@article{fdi:010095534,
  title = {{T}wo-stage cluster sampling to assess {SARS}-{C}o{V}-2 seroprevalence without pre-enumeration : an example from {M}adagascar},
  author = {{L}orenz, {E}. and {A}muasi, {J}. and {R}andrianarisoa, {T}. and {R}asamoelina, {T}. and {G}unga, {L}. and {B}enke, {D}. and {S}tr{\¨o}bele, {J}. and {K}ettenbeil, {J}. and {L}oag, {W}. and {A}ndriamahandry, {H}. and {R}azanakolona, {L}. and {R}andrianirina, {J}. {R}. and {R}andrianasolo, {H}. and {R}atombotsoa, {J}. {C}. and {N}ahita, {F}. and {E}ibach, {D}. and {F}usco, {D}. and {M}ay, {J}. and {R}akotoarivelo, {R}. {A}. and {S}ouares, {A}. and {B}onnet, {E}mmanuel and {S}truck, {N}. {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{I}mplementing population-based surveys in resource-constrained settings presents logistical challenges when detailed population enumeration is unavailable. {W}e developed a field mapping system integrated into a cluster sampling approach to eliminate pre-enumeration requirements for a {SARS}-{C}o{V}-2 seroprevalence survey in {M}adagascar. {W}e conducted a cross-sectional observational study in urban {F}ianarantsoa, {M}adagascar, between {F}ebruary and {J}une 2021. {U}sing probability proportional to size sampling, we selected clusters from administrative areas (fokontany) and randomly generated {GPS} coordinates within these clusters. {F}ield teams navigated to coordinates using {O}pen{S}treet{M}ap software on tablets, identified eligible households, and conducted health surveys with blood sampling. {W}e employed a mobile-compatible system for real-time household mapping and data collection, functioning without continuous network connectivity. {S}ample size calculation targeted 650 households ({SARS}-{C}o{V}-2 seroprevalence 30%, precision +/- 5%, design effect 2.0). {O}ur specific objectives were to develop and implement a geographic cluster sampling method that did not require pre-enumeration; to assess the feasibility of this method through participation rates; and to evaluate potential selection biases related to socioeconomic factors. {W}e identified households at 95.3% (696/730) of randomly generated {GPS} coordinates. {O}f contacted households, 96.8% (674/696) participated, representing 1,121 individuals across 57 clusters. {P}articipation rates varied geographically, with a modest inverse correlation with household wealth (participation decreased by 0.85% per wealth quintile increase, 95% {CI}: -3.54% to 1.84%). {D}emographic characteristics of our sample matched census data for urban {F}ianarantsoa, supporting the representativeness of our approach. {T}his integrated field mapping system created a virtual household map simultaneously with survey implementation, enabling cost-effective two-stage cluster sampling without pre-enumeration. {T}he approach enabled evaluation of selection bias, simplified logistics, and provided a permanent geo-referenced database of surveyed households. {T}his methodology offers a practical solution for population-based surveys in resource-constrained settings with incomplete enumeration data and has applications beyond {COVID}-19 research for various public health surveillance activities.},
  keywords = {{MADAGASCAR}},
  booktitle = {},
  journal = {{PL}o{S} {O}ne},
  volume = {20},
  numero = {11},
  pages = {e0334627 [11 p.]},
  year = {2025},
  DOI = {10.1371/journal.pone.0334627},
  URL = {https://www.documentation.ird.fr/hor/fdi:010095534},
}